Michele on Software

My opinions about software product development

Why Your Codebase Fights Change (And How to Fix It)

If you’ve worked with software for any length of time, you’ve seen it: a codebase that starts as a lean, focused system can morph into a costly, resistant beast without careful stewardship. I recently led a workshop on this topic, and it sharpened my perspective on what drives long-term success in development. What’s difficult isn’t implementing algorithms or optimizing data structures. It’s about ensuring that ongoing changes remain affordable, low-risk, and efficient. For those steering teams through the pressures of tight deadlines, growing systems, and collaborative demands, managing complexity is what keeps projects on track, budgets in check, and teams productive.

What Development Really Entails

At its core, software development is about adapting a system’s behavior to meet new needs. This happens continuously, in response to the business chasing opportunities or addressing risks. Think of changes in behaviour as a series of decisions: when a certain event occurs, if specific conditions hold, then execute a particular action. An example is “when a transaction completes, if a user has enabled this, then they receive a notification that this happened”.

Simple in theory, but the same change can be a quick win in one codebase and a costly slog in another. Why? Because the work isn’t just coding the new logic, but also ensuring that the rest of the system continues to function as expected.

Every change requires:

  1. Identifying the trigger points where the update applies.
  2. Evaluating conditions, often entangled with state management.
  3. Implementing the new behavior.
  4. Verifying the change delivers as intended.
  5. Confirming existing functionality remains intact.

Complexity is what turns these steps into a grind, especially when multiple contributors are involved. Left unchecked, it inflates timelines, escalates risks, and drains resources. The real challenge of software development is keeping complexity bounded as the system evolves, ensuring teams can iterate without sinking into a maintenance nightmare.

Understanding Complexity’s Causes

Complexity isn’t an abstract concept, but the friction that slows teams down and spikes costs. It stems from three key factors, all tied to how code is structured:

  1. Dependencies: Which can make a small change ripple unpredictably across the system. You want a banana, but it comes with a gorilla, which in turn comes with the jungle.
  2. Cognitive load: The amount of things you must juggle in your mind to operate the change.
  3. Unknown unknowns: Hidden behaviors or edge cases that surface at the worst possible moments.

These factors don’t scale linearly. A system twice as large is typically more than twice as complex. Modularity is the antidote, breaking code into smaller, self-contained units to limit the scope of any single change. A module, whether a function, class, or library, has a contract (what it does) and an implementation (how it does it). A strong contract hides the “how,” using clear specifications like types to minimize vague expectations.

Modules deliver by:

  1. Reducing cognitive load: Teams only need to grasp the contract to use a module, not its guts unless you’re changing it.
  2. Clarifying dependencies: Explicit connections make systems easier to reason about and maintain.
  3. Containing changes: Localized updates prevent cascading side effects, protecting stability.

Shifting to Strategic Development

It’s easy to prioritize speed and churn out features to hit deadlines. But that short-term speed leads to long-term slowdowns. Strategic development emphasizes structures that make future changes cheaper and safer. Every tweak to behavior is a chance to refine the system’s structure and architecture. Without care, you’re stacking up complexity that inflates future costs, delays market delivery, and erodes competitive edge.

Below are some development principles, grouped for clarity, to guide a strategic approach drawn from real-world experience:

Organizing for Clarity

Clear, intentional structure reduces confusion and speeds up development.

  1. Structure the codebase as a graph of modules with clear dependencies, avoiding chaotic, flat namespaces.
  2. Eliminate circular dependencies, cycles between files or modules, because they create fragility that slows progress.
  3. Name elements after business or domain concepts, not technical specifics, for clarity.
  4. Balance high cohesion (grouping related logic) with low coupling (reducing inter-module reliance).
  5. Ensure APIs and structures are discoverable without deep investigation. As an example, avoid static functions, and inject dependencies through constructors.

Reducing Risks

Proactive design choices minimize errors and unexpected impacts.

  1. Maximize formal requirements (like those enforced by a type system) to reduce ambiguous expectations.
  2. Design function signatures to accommodate new data without forcing widespread updates.
  3. Request all potential decision-making data upfront, not just what’s needed today, to avoid rework.
  4. Build and test against module contracts, not internal details, to decouple components.
  5. Write intuitive code that minimizes surprises for team members. Avoid surprises, and use sensible defaults that work in most cases.
  6. Document informal requirements thoroughly to eliminate guesswork. In particular, document contracts extensively with their preconditions, checks, thrown errors, etc.

Enabling Efficient Changes

Processes and patterns can make modifications predictable and safe.

  1. Simplify logic with patterns like polymorphism or factories instead of nested conditionals.
  2. Start with a test demonstrating the desired behavior, then work backward to contract and implementation.
  3. Define contracts and interactions before coding the details.
  4. Break changes into small, incremental steps to minimize risk.
  5. Adopt clear packaging patterns, like Ports and Adapters, to make intent transparent.

Strengthening Team Practices

Workflows and approaches can prevent entire classes of issues.

  1. Test based on dependency chains. Retest dependent modules when their dependencies change, not vice versa. If A depends on B, test A when B changes, changes in A shouldn’t require retesting B.
  2. Restrict module contexts, like keeping database logic out of API endpoints, to prevent misuse.
  3. Use practices like Test-Driven Development, Domain-Driven Design, Event Sourcing, or Continuous Integration to make entire categories of errors impossible.
  4. Embrace the mindset: make the change easy, and then make the easy change (as Kent Beck eloquently put it).

Implications for Leadership

Managing complexity isn’t just a coding problem, but a strategic imperative that shapes processes, team dynamics, and business outcomes. Uncontrolled complexity can enormously increase maintenance costs, delay market delivery, or erode customer satisfaction, undermining your competitive edge. Here’s how to apply these principles in practice:

Process Alignment

  1. Embed modularity and strategic principles into workflows.
  2. Encourage small, incremental changes and rigorous testing to catch issues early.
  3. Use Continuous Integration pipelines to enforce dependency rules and contract-based testing, reducing regressions.

Team Empowerment

  1. Foster a culture where developers prioritize long-term maintainability over quick fixes.
  2. Provide training on modular design and tools like static analysis to catch complexity creep.
  3. Set clear documentation standards for informal requirements to save countless hours of confusion.

Ditch Arbitrary Deadlines

  1. Avoid blanket deadlines, as most initiatives don’t have sharp cost-of-delay curves.
  2. Recognize that pressure leads to shortcuts and neglect of structure—slightly quicker today, significantly slower tomorrow.

Resource Allocation

  1. Invest in refactoring and technical debt reduction as regular work, not an afterthought.
  2. Budget time for defining contracts and structuring codebases to prevent exponential cost increases later.

Performance Management

  1. Shift from lines of code or features shipped to continuously improving quality and reducing the cost of change.
  2. Track cognitive load through developer feedback on codebase clarity and ease of modification.

Collaboration

  1. Promote cross-functional alignment by treating the team as the unit of work allocation.
  2. Ensure accountability for high-quality implementations and bridge technical and business concerns.

Closing Thoughts: Building Systems That Last

Taming complexity is about intentionality. By embedding these principles into your processes, you create codebases that support rapid iteration, scale gracefully, and don’t fall apart under continuous change. This isn’t about perfection but equipping teams to deliver value consistently without burning out or blowing budgets.

Look at your current projects: where’s complexity creeping in? Maybe it’s tangled dependencies or unclear module boundaries. Pick one area, apply a principle like explicit dependencies or test-first design, and observe the impact. What’s the worst complexity mess you’ve faced, and how did you tackle it? Share your experiences in the comments to keep this conversation alive.

Here’s to building software that evolves without fighting back!